Radio Frequency Identification (RFID) in health care: where are we? A scoping review

Purpose: (RFID) is a technology that uses radio waves for data collection and transfer, so data is captured efficiently, automatically and in real time without human intervention. This technology, alone or in addition to other technologies has been considered as a possible solution to reduce problems that endanger public health or to improve its management. This scoping review aims to provide readers with an up-to-date picture of the use of this technology in health care settings. Methods: This scoping review examines the state of RFID technology in the healthcare area for the period 2017-2022, specifically addressing RFID versatility and investigating how this technology can contribute to radically change the management of public health. The guidelines of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) have been followed. Literature reviews or surveys were excluded. Only articles describing technologies implemented on a real environment or on prototypes were included. Results: The search returned 366 results. After screening, based on title and abstract, 58 articles were considered suitable for this work. 11 articles were reviewed because they met the qualifying requirements. The study of the selected articles highlighted six matters that can be profitably impacted by this technology Conclusion: The selected papers show that this technology can improve patient safety by reducing medical errors, that can occur within operating rooms. It can also be the solution to overcome the problem of the black market in counterfeiting drugs, or as a prevention tool. Further research is needed, especially on data management, security, and privacy, given the sensitive nature of medical information. Graphical Abstract: [Figure not available: see fulltext.

Evidence-based medical equipment management: a convenient implementation

Maintenance is a crucial subject in medical equipment life cycle management. Evidence-based maintenance consists of the continuous performance monitoring of equipment, starting from the evidence-the current state in terms of failure history-and improvement of its effectiveness by making the required changes. This process is very important for optimizing the use and allocation of the available resources by clinical engineering departments. Medical equipment maintenance is composed of two basic activities: scheduled maintenance and corrective maintenance. Both are needed for the management of the entire set of medical equipment in a hospital. Because the classification of maintenance service work orders reveals specific issues related to frequent problems and failures, specific codes have been applied to classify the corrective and scheduled maintenance work orders at Careggi University Hospital (Florence, Italy). In this study, a novel set of key performance indicators is also proposed for evaluating medical equipment maintenance performance. The purpose of this research is to combine these two evidence-based methods to assess every aspect of the maintenance process and provide an objective and standardized approach that will support and enhance clinical engineering activities. Starting from the evidence (i.e. failures), the results show that the combination of these two methods can provide a periodical cross-analysis of maintenance performance that indicates the most appropriate procedures. Graphical abstract The left side shows a block diagram of the process needed to calculate the proposed set of KPIs, starting from technological, organizational and financial data. On the upper right it is shown an example of scheduled maintenance analysis for a specific class of equipment (legend in the article body). The bottom right part shows how the KPIs can be implemented in a business intelligence dashboard

An automatic system supporting clinical decision for chronic obstructive pulmonary disease

This paper presents a system supporting clinical decisions for patients with Chronic Obstructive Pulmonary Disease (COPD). The system should partially fill the gaps highlighted during an analysis of the current state of the art of Clinical Decision Support Systems (CDSS) for telemonitoring patients affected by COPD. The first step taken was to replicate the performance of similar decision support systems found in the scientific literature. Using physiological parameters drawn from respiratory function tests on 414 patients, two predictive models were created using two machine-learning algorithms: neural network and support vector machine. Performance was comparable to that described in the literature. The results made it possible to affirm that the data available were sufficient to evaluate the extent of respiratory deficit. The next step was to create a new predictive model with better performance than previously obtained. The C5.0 Machine Learning Algorithm was chosen for the development of the model. The resulting performance on the data available was significantly better than with the two previous models. This new predictive model, called COPD, was then implemented in a user interface created using Java programming language. The new software developed, which enables the evaluation and classification of respiratory test results and which can be used in many clinical applications, provides excellent performance compared to the current state of the art

Reduced Power Multi-Cell Multicast Transmission

Future 5G networks will require the reduction of the energy consumption despite the increase of connected devices and data traffic. This paper focuses on an efficient coordinated multicast transmission in Ultra Dense Networks. In particular, a new iterative algorithm is proposed, whose goal is the minimization of the total transmitted power while guaranteeing a minimum received signal-to-noise-plus-interference ratio to all users belonging to the multicast group. Small cells are opportunistically activated, using adaptive transmission power and beamforming weights, to increase the efficiency of the radiated power. The effectiveness of the proposed method is proven in terms of transmitted power and energy efficiency in comparison with different benchmark solutions

Network service description model for VNF orchestration leveraging intent-based SDN interfaces

Network Function Virtualization and Software Defined Networking promise to radically innovate the way network services are provisioned, especially in terms of dynamic and flexible service delivery. Although the problem of NFV/SDN orchestration is recently gaining increasing interest, the problem of how effectively realizing the as-a-service exposure of network functions and services remains essentially uninvestigated. Leveraging service-oriented principles and best practices, in this paper we propose a two-layer service-oriented description model and a logical architecture for network service provisioning distinguishing business and orchestration scopes. Our proposal takes into account ETSI specifications for NFV orchestration and intent-based abstractions for the SDN controller northbound interface. We present a preliminary validation of the proposed approach focusing on the orchestration layer, based on a Proof of Concept development and testing. Finally, we discuss future work

Modeling of Pupillometric Signals for Studying Children’s Rare Diseases

The use of chromatic pupil responses may be a novel way to diagnose and monitor diseases affecting either the outer or inner retina. Here we developed a model-based approximation of the dynamics of pupillary diameter after an optical stimulation; we assume the response of the pupil is approximated as the output of a 2nd-order linear model. Model parameters are identified by using a least-squares fitting procedure, thus obtaining an optimal estimate of the activation time and of the shape of the pupil response to the stimulation. Results indicate the model adequately represents the curve shape; despite the presence of artifacts, that hinder the fitting procedure, a significant difference in the time constants of the model of controls and of patients is present